A network analysis to identify mediators of germline-driven differences in breast cancer prognosis
(2020) In Nature Communications 11(1).- Abstract
- Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as... (More)
- Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis. © 2020, The Author(s). (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e52ae25c-0b44-4f16-a168-4e108614c605
- author
- Escala-Garcia, Maria ; Brenner, Hermann LU ; Ellberg, Carolina LU ; Olsson, Håkan LU ; Hall, Per LU ; Canisius, Sander and Schmidt, Marjanka K
- author collaboration
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Nature Communications
- volume
- 11
- issue
- 1
- article number
- 312
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85077940819
- pmid:31949161
- ISSN
- 2041-1723
- DOI
- 10.1038/s41467-019-14100-6
- language
- English
- LU publication?
- yes
- id
- e52ae25c-0b44-4f16-a168-4e108614c605
- date added to LUP
- 2020-01-27 14:04:41
- date last changed
- 2024-01-31 14:22:33
@article{e52ae25c-0b44-4f16-a168-4e108614c605, abstract = {{Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis. © 2020, The Author(s).}}, author = {{Escala-Garcia, Maria and Brenner, Hermann and Ellberg, Carolina and Olsson, Håkan and Hall, Per and Canisius, Sander and Schmidt, Marjanka K}}, issn = {{2041-1723}}, language = {{eng}}, number = {{1}}, publisher = {{Nature Publishing Group}}, series = {{Nature Communications}}, title = {{A network analysis to identify mediators of germline-driven differences in breast cancer prognosis}}, url = {{http://dx.doi.org/10.1038/s41467-019-14100-6}}, doi = {{10.1038/s41467-019-14100-6}}, volume = {{11}}, year = {{2020}}, }